Advances in pancreatic cancer biomarkers

Recent Trends and Innovations in Bead-Based Biosensors for Cancer Detection

Demand is strong for sensitive, reliable, and cost-effective diagnostic tools for cancer detection. Accordingly, bead-based biosensors have emerged in recent years as promising diagnostic platforms based on wide-ranging cancer biomarkers owing to the versatility, high sensitivity, and flexibility to perform the multiplexing of beads. This comprehensive review highlights recent trends and innovations in the development of bead-based biosensors for cancer-biomarker detection. We introduce various types of bead-based biosensors such as optical, electrochemical, and magnetic biosensors, along with their respective advantages and limitations. Moreover, the review summarizes the latest advancements, including fabrication techniques, signal-amplification strategies, and integration with microfluidics and nanotechnology. Additionally, the challenges and future perspectives in the field of bead-based biosensors for cancer-biomarker detection are discussed. Understanding these innovations in bead-based biosensors can greatly contribute to improvements in cancer diagnostics, thereby facilitating early detection and personalized treatments.

An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer

Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer-related deaths in the United States due to the lack of early diagnosis and extremely poor survival. Despite great advancements in biomedical research, there are very limited early diagnostic modalities available for the early detection of PanCa. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority. Herein, we have utilized high dimensional omics data along with some wet laboratory experiments to decipher the expression level of hormone receptor interactor 13 (TRIP13) in various pathological staging including functional enrichment analysis. The functional enrichment analyses specifically suggest that TRIP13 and its related oncogenic network genes are involved in very important patho-physiological pathways. These analyses are supported by qPCR, immunoblotting and IHC analysis. Based on our study we proposed TRIP13 as a novel molecular target for PanCa diagnosis and therapeutic interventions. Overall, we have demonstrated a crucial role of TRIP13 in pathogenic events and progression of PanCa through applied integrated computational biology approaches.

Prognostic utility of TME-associated genes in pancreatic cancer

Background: Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focuses on the assessment of the prognostic and treatment utility of TME-associated genes in PC. Methods: After obtaining the differentially expressed TME-related genes, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on The Cancer Genome Atlas (TCGA) data set, and it was validated by external data sets from the Gene Expression Omnibus (GEO) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Multiomics analyses were adopted to explore the potential mechanisms, discover novel treatment targets, and assess the sensitivities of immunotherapy and chemotherapy. Results: Five TME-associated genes, namely, FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and a risk score formula constructed. Next, their mRNA expressions were verified in cancer and normal pancreatic cells. Multiple algorithms confirmed that the risk model displayed a reliable ability of prognosis prediction and was an independent prognostic factor, indicating that high-risk patients had poor outcomes. Immunocyte infiltration, gene set enrichment analysis (GSEA), and single-cell analysis all showed a strong relationship between immune mechanism and low-risk samples. The risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, which included oxaliplatin and irinotecan. Various latent treatment targets (LAG3, TIGIT, and ARID1A) were addressed by mutation landscape based on the risk model. Conclusion: The risk model based on TME-related genes can reflect the prognosis of PC patients and functions as a novel set of biomarkers for PC therapy.

Holistic Understanding of the Role of Carbohydrate Antigen 19-9 in Pancreatic Cancer Screening, Early Diagnosis, and Prognosis: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) is a significant challenge due to its silent progression and well-advanced, unresectable, complicated presentation. Detecting this disease early on is crucial, and researchers have been investigating various potential biological markers, such as carbohydrate antigen 19-9 (CA 19-9), hoping to find indicators that can aid in its early detection. The primary focus of this review is on the diagnostic usefulness of CA 19-9 in detecting pancreatic cancer (PC) in the beginning stage and its usefulness in predicting progression. The database search of articles from PubMed, PMC, the Cochrane Library, and Google Scholar identified 227 articles published from 2013 to 2023. The keyword mix used in the search technique included terms like "CA 19-9," "pancreatic cancer," "diagnosis," and "early detection." This study provides evidence of CA 19-9's ability in detecting PDAC in the pre-diagnostic stage. But since the outcomes were inconsistent among the included trials, further analysis is required to develop standardized diagnostic criteria and methodologies. Furthermore, because of the variability of the study, it is not easy to make firm conclusions on CA 19-9's sensitivity as well as specificity in the first stage of pancreatic neoplasm. This in-depth overview of the available literature provides new insights into using CA 19-9 as a biological marker for detecting undiagnosed PC before progressing into the advanced stage, and was proven beneficial. However, this has to be shown in broader research with adequate sample size. Although it shows promise as a diagnostic tool, further study is required to confirm these findings.

Advances in the Early Diagnosis of Pancreatic Ductal Adenocarcinoma and Premalignant Pancreatic Lesions

Pancreatic cancer is one of the most lethal human malignancies, in part because it is often diagnosed at late stages when surgery and systemic therapies are either unfeasible or ineffective. Therefore, diagnosing pancreatic cancer in earlier stages is important for effective treatment. However, because the signs and symptoms may be nonspecific and not apparent until the disease is at a late stage, the timely diagnoses of pancreatic cancer can be difficult to achieve. Recent studies have shown that selective screening and increased usage of biomarkers could improve the early diagnosis of pancreatic cancer. In this review, we discuss recent advancements in the early detection of pancreatic ductal carcinoma and precancerous lesions. These include innovations in imaging modalities, the diagnostic utility of various biomarkers, biopsy techniques, and population-based surveillance approaches. Additionally, we discuss how machine learning methods are being applied to develop integrated methods of identifying individuals at high risk of developing pancreatic disease. In the future, the overall survival of pancreatic cancer patients could be improved by the development and adoption of these new methods and techniques.

Uncovering Tumor-Promoting Roles of Activin A in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with high incidence rates of metastasis and cachexia. High circulating activin A, a homodimer of inhibin βA subunits that are encoded by INHBA gene, predicts poor survival among PDAC patients. However, it still raises the question of whether activin A suppression renders favorable PDAC outcomes. Here, the authors demonstrate that activin A is abundantly detected in tumor and stromal cells on PDAC tissue microarray and mouse PDAC sections. In orthotopic male mice, activin A suppression, which is acquired by tumor-targeted Inhba siRNA using cholesterol-modified polymeric nanoparticles, retards tumor growth/metastasis and cachexia and improves survival when compared to scramble siRNA-treated group. Histologically, activin A suppression coincides with decreased expression of proliferation marker Ki67 but increased accumulation of α-SMAhigh fibroblasts and cytotoxic T cells in the tumors. In vitro data demonstrate that activin A promotes KPC cell proliferation and induces the downregulation of α-SMA and upregulation of IL-6 in pancreatic stellate cells (PSC) in the SMAD3-dependent mechanism. Moreover, conditioned media from activin A-stimulated PSC promoted KPC cell growth. Collectively, our data provide a mechanistic basis for tumor-promoting roles of activin A and support therapeutic potentials of tumor activin A suppression for PDAC.

Progress on diagnostic and prognostic markers of pancreatic cancer

Pancreatic cancer is a malignant disease characterized by low survival and high recurrence rate, whose patients are mostly at the stage of locally advanced or metastatic disease when first diagnosed. Early diagnosis is particularly important because prognostic/predictive markers help guide optimal individualized treatment regimens. So far, CA19-9 is the only biomarker for pancreatic cancer approved by the FDA, but its effectiveness is limited by low sensitivity and specificity. With recent advances in genomics, proteomics, metabolomics, and other analytical and sequencing technologies, the rapid acquisition and screening of biomarkers is now possible. Liquid biopsy also occupies a significant place due to its unique advantages. In this review, we systematically describe and evaluate the available biomarkers that have the greatest potential as vital tools in diagnosing and treating pancreatic cancer.

Targeted EpCAM-binding for the development of potent and effective anticancer proteins

Protein-based cancer therapies are considered an alternative to conventional anticancer regimens, providing multifunctional properties while showing low toxicity. However, its widespread use is limited by absorption and instability issues, resulting in higher dosage requirements and a prolonged onset of bioactivity to elicit the desired response. Here, we developed a non-invasive antitumor treatment using designed ankyrin repeat protein (DARPin)-anticancer protein-conjugate that specifically targets the cancer biomarker, epithelial cell adhesion molecule (EpCAM). The DARPin-anticancer proteins bind to EpCAM-positive cancer cells and improve the in vitro anticancer efficacy by over 100-folds within 24 h, where the DARPin-tagged human lactoferrin fragment (drtHLF4) IC50 value is within the nanomolar range. Orally administered drtHLF4 was readily absorbed into the systemic flow of the HT-29 cancer murine model, exerting its anticancer effect on other tumors in the host body. Orally administered drtHFL4 cleared HT29-colorectal tumors using a single dose, whereas intratumoral injection cleared HT29-subcutaneous tumors within three doses. This approach addresses the limitations of other protein-based anticancer treatments by providing a non-invasive anticancer therapy with improved potency and tumor-specificity.

Correlation between ADC Histogram-Derived Metrics and the Time to Metastases in Resectable Pancreatic Adenocarcinoma

Background: A non-invasive method to improve the prognostic stratification would be clinically beneficial in patients with resectable pancreatic adenocarcinoma (PDAC). The aim of this study was to correlate conventional magnetic resonance (MR) features and the metrics derived from the histogram analysis of apparent diffusion coefficient (ADC) maps, with the risk and the time to metastases (TTM) after surgery in patients with PDAC. Methods: pre-operative MR examinations of 120 patients were retrospectively analyzed. Patients were grouped according to the presence (M+) or absence (M−) of metastases during follow-up. Conventional MR features and histogram-derived metrics were compared between M+ and M− patients using the Fisher’s or Mann−Whitney tests; receiver operating characteristic (ROC) curves were constructed for the features that showed a significant difference between groups. A Cox regression analysis was performed to identify the features with a significant effect on the TTM, and Kaplan−Meier curves were constructed for significant features. Results: 68.3% patients developed metastases over a mean follow-up time of 29 months (range, 3−54 months). ADC skewness and kurtosis were significantly higher in M+ than in M− patients (p < 0.001). Skewness had a significant effect on the risk of metastases (hazard ratio—HR = 5.22, p < 0.001). Patients with an ADC skewness ≥0.23 had a significantly shorter TTM than those with a skewness <0.22 (11.7 vs. 30.8 months, p < 0.001). Conclusions: pre-operative histogram analysis of ADC maps provides parameters correlated to the metastatic potential of PDAC. Higher ADC skewness seems to be associated with a significantly shorter TTM in patients with resectable PDAC.

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images

PURPOSE

To explore a multidomain fusion model of radiomics and deep learning features based on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis.

MATERIALS AND METHODS

This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on ¹⁸F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models.

RESULTS

The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model.

CONCLUSION

The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on ¹⁸F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.

Human Cyclophilin B Nuclease Activity Revealed via Nucleic Acid-Based Electrochemical Sensors

Human cyclophilin B (CypB) is oversecreted by pancreatic cancer cells, making it a potential biomarker for early-stage disease diagnosis. Our group is motivated to develop aptamer-based assays to measure CypB levels in biofluids. However, human cyclophilins have been postulated to have collateral nuclease activity, which could impede the use of aptamers for CypB detection. To establish if CypB can hydrolyze electrode-bound nucleic acids, we used ultrasensitive electrochemical sensors to measure CypB's hydrolytic activity. Our sensors use ssDNA and dsDNA in the biologically predominant d-DNA form, and in the nuclease resistant l-DNA form. Challenging such sensors with CypB and control proteins, we unequivocally demonstrate that CypB can cleave nucleic acids. To our knowledge, this is the first study to use electrochemical biosensors to reveal the hydrolytic activity of a protein that is not known to be a nuclease. Future development of CypB bioassays will require the use of nuclease-resistant aptamer sequences.

Prognostic value of systemic immune-inflammation index in patients with pancreatic cancer: a meta-analysis

The systemic immune-inflammation index (SII) is a significant prognostic factor in some cancer types. However, the prognostic value of SII in patients with pancreatic cancer (PC) remains controversial. This study aimed to evaluate the prognostic impact of SII in patients with PC through a meta-analysis. This meta-analysis is aimed to investigate the prognostic significance of SII in patients with PC. Relevant articles were obtained through a systematic search. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to estimate the association between SII and survival outcomes, including overall survival (OS), cancer-specific survival (CSS), disease-free survival (DFS), and progression-free survival (PFS). Seven studies with 2132 patients were included in the meta-analysis. The results revealed that elevated pretreatment SII was associated with poor OS (HR = 1.55, 95% CI: 1.34-1.78, p < 0.001) and inferior CSS/DFS/PFS (HR = 1.51, 95% CI: 1.27-1.80, p < 0.001). The prognostic role was reliable in a subgroup analysis conducted according to regions, disease status, survival analysis, and cutoff value. High SII was associated with poor OS in patients with PC. Therefore, SII is suggested to be a cost-effective biological marker for monitoring survival in patients with PC.

Current and future applications of biomarkers in samples collected through minimally invasive methods for cancer medicine and population-based research

Despite advances in cancer medicine and research, invasive and potentially risky procedures such as biopsies, venous blood tests, imaging, colonoscopy, and pap smear tests are still primarily used for screening, staging, and assessing response to therapy. The development and interdisciplinary use of biomarkers from urine, feces, saliva, scent, and capillary blood collected with minimally invasive methods represents a potential opportunity for integration with biomarker analysis for cancers, both in clinical practice (e.g., in screening, treatment, and disease monitoring, and improved quality of life for patients) and population-based research (e.g., in epidemiology/public health, studies of social and environmental determinants, and evolutionary medicine). In this article, we review the scientific rationale, benefits, challenges, and potential opportunities for measuring cancer-related biomarkers in samples collected through minimally invasive methods.

Circular RNAs Involved in the Regulation of the Age-Related Pathways

Circular RNAs (circRNAs) are a class of covalently circular noncoding RNAs that have been extensively studied in recent years. Aging is a process related to functional decline that is regulated by signal transduction. An increasing number of studies suggest that circRNAs can regulate aging and multiple age-related diseases through their involvement in age-related signaling pathways. CircRNAs perform several biological functions, such as acting as miRNA sponges, directly interacting with proteins, and regulating transcription and translation to proteins or peptides. Herein, we summarize research progress on the biological functions of circRNAs in seven main age-related signaling pathways, namely, the insulin-insulin-like, PI3K-AKT, mTOR, AMPK, FOXO, p53, and NF-κB signaling pathways. In these pathways, circRNAs mainly function as miRNA sponges. In this review, we suggest that circRNAs are widely involved in the regulation of the main age-related pathways and are potential biomarkers for aging and age-related diseases.

A faecal microbiota signature with high specificity for pancreatic cancer

BACKGROUND

Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.

OBJECTIVE

To explore the faecal and salivary microbiota as potential diagnostic biomarkers.

METHODS

We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case-control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case-control study (n=76), in the validation phase.

RESULTS

Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19-9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.

CONCLUSION

Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

BUB1 Is Identified as a Potential Therapeutic Target for Pancreatic Cancer Treatment

Pancreatic cancer is one of the most challenging cancer types in clinical treatment worldwide. This study aimed to understand the tumorigenesis mechanism and explore potential therapeutic targets for patients with pancreatic cancer. Single-cell data and expression profiles of pancreatic cancer samples and normal tissues from multiple databases were included. Comprehensive bioinformatics analyses were applied to clarify tumor microenvironment and identify key genes involved in cancer development. Immense difference of cell types was shown between tumor and normal samples. Four cell types (B cell_1, B cell_2, cancer cell_3, and CD1C+_B dendritic cell_3) were screened to be significantly associated with prognosis. Three ligand-receptor pairs, including CD74-MIF, CD74-COPA, and CD74-APP, greatly contributed to tumorigenesis. High expression of BUB1 (BUB1 Mitotic Checkpoint Serine/Threonine Kinase) was closely correlated with worse prognosis. CD1C+_B dendritic cell_3 played a key role in tumorigenesis and cancer progression possibly through CD74-MIF. BUB1 can serve as a prognostic biomarker and a therapeutic target for patients with pancreatic cancer. The study provided a novel insight into studying the molecular mechanism of pancreatic cancer development and proposed a potential strategy for exploiting new drugs.

Performance evaluation of the Elecsys PIVKA‐II and Elecsys AFP assays for hepatocellular carcinoma diagnosis

Background and Aims

Prothrombin induced by vitamin K absence‐II (PIVKA‐II) is a serum biomarker linked to hepatocellular carcinoma (HCC), showing superiority to alpha‐fetoprotein (AFP) for early disease detection. We aimed to assess the clinical and analytical performance of the Elecsys® PIVKA‐II immunoassay in diagnosing HCC and evaluate PIVKA‐II's technical performance.

Methods

Serum samples from adult cases (i.e. patients with a first‐time HCC diagnosis; n = 168) and disease controls (i.e. patients without HCC with an at‐risk condition; n = 208) were assessed. An AFP cut‐off of 20 ng/mL was used to differentiate between HCC cases and disease controls. Clinical performance of the Elecsys PIVKA‐II assay was compared with that of comparator assays (Lumipulse G PIVKA‐II, μTASWako DCP, ARCHITECT PIVKA‐II) using receiver operating characteristic curve analysis to determine the area under the curve (AUC) values.

Results

The Elecsys PIVKA‐II assay compared favorably with comparator assays. Using a 28.4 ng/mL cut‐off, the Elecsys PIVKA‐II assay detected HCC with 86.9% sensitivity and 83.7% specificity. Clinical performance of the Elecsys PIVKA‐II assay (AUC: 90.8%) was equivalent to that of comparator assays (AUC: 88.3–89.6%). Relatively high PIVKA‐II concentrations were observed for cholangiocarcinoma and pancreatic cancer with the Elecsys assay in specificity panel analyses, indicating that high PIVKA‐II concentrations should not be used alone in the absence of other clinical data.

Conclusions

The Elecsys PIVKA‐II assay showed good analytical performance under routine laboratory conditions, comparing favorably with comparator assays. These findings support the suitability of the Elecsys PIVKA‐II assay as an aid in HCC diagnosis.

A Computational Approach to Justifying Stratifin as a Candidate Diagnostic and Prognostic Biomarker for Pancreatic Cancer

Pancreatic cancer (PC) is considered a silent killer because it does not show specific symptoms at an early stage. Thus, identifying suitable biomarkers is important to avoid the burden of PC. Stratifin (SFN) encodes the 14-3-3σ protein, which is expressed in a tissue-dependent manner and plays a vital role in cell cycle regulation. Thus, SFN could be a promising therapeutic target for several types of cancer. This study was aimed at investigating, using online bioinformatics tools, whether SFN could be used as a diagnostic and prognostic biomarker in PC. SFN expression was explored by utilizing the ONCOMINE, UALCAN, GEPIA2, and GENT2 tools, which revealed that SFN expression is higher in PC than in normal tissues. The clinicopathological analysis using the ULCAN tool showed that the intensity of SFN expression is commensurate with cancer progression. GEPIA2, R2, and OncoLnc revealed a negative correlation between SFN expression and survival probability in PC patients. The ONCOMINE, UCSC Xena, and GEPIA2 tools showed that cofilin 1 is strongly coexpressed with SFN. Moreover, enrichment and network analyses of SFN were performed using the Enrichr and NetworkAnalyst platforms, respectively. Receiver operating characteristic (ROC) curves revealed that tissue-dependent expression of the SFN gene could serve as a diagnostic and prognostic biomarker. However, further wet laboratory studies are necessary to determine the relevance of SFN expression as a biomarker.

CDC25C is a prognostic biomarker and correlated with mitochondrial homeostasis in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a common digestive tract malignant tumor with an extremely poor prognosis. The survival and prognosis may significantly improve if it is diagnosed early. Therefore, identifying biomarkers for early diagnosis is still considered a great clinical challenge in PAAD. Cell Division Cycle 25C (CDC25C), a cardinal cell cycle regulatory protein, directly mediates the G2/M phase and is intimately implicated in tumor development. In the current study, we aim to explore the possible functions of CDC25C and determine the potential role of CDC25C in the early diagnosis and prognosis of PAAD. Expression analysis indicated that CDC25C was overexpressed in PAAD . In addition, survival analysis revealed a strong correlation between the enhanced expression of CDC25C and poor survival in PAAD. Furthermore, pathway analysis showed that CDC25C is related to TP53 signaling pathways, glutathione metabolism, and glycolysis. Mechanically, our in vitro experiments verified that CDC25C was capable of promoting cell viability and proliferation. CDC25C inhibition increases the accumulation of ROS, inhibits mitochondrial respiration, suppresses glycolysis metabolism and reduces GSH levels. To summarize, CDC25C may be involved in energy metabolism by maintaining mitochondrial homeostasis. Our results suggested that CDC25C is a potential biological marker and promising therapeutic target of PAAD.

Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma

Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.

Advances in High Throughput Proteomics Profiling in Establishing Potential Biomarkers for Gastrointestinal Cancer

Gastrointestinal cancers (GICs) remain the most diagnosed cancers and accounted for the highest cancer-related death globally. The prognosis and treatment outcomes of many GICs are poor because most of the cases are diagnosed in advanced metastatic stages. This is primarily attributed to the deficiency of effective and reliable early diagnostic biomarkers. The existing biomarkers for GICs diagnosis exhibited inadequate specificity and sensitivity. To improve the early diagnosis of GICs, biomarkers with higher specificity and sensitivity are warranted. Proteomics study and its functional analysis focus on elucidating physiological and biological functions of unknown or annotated proteins and deciphering cellular mechanisms at molecular levels. In addition, quantitative analysis of translational proteomics is a promising approach in enhancing the early identification and proper management of GICs. In this review, we focus on the advances in mass spectrometry along with the quantitative and functional analysis of proteomics data that contributes to the establishment of biomarkers for GICs including, colorectal, gastric, hepatocellular, pancreatic, and esophageal cancer. We also discuss the future challenges in the validation of proteomics-based biomarkers for their translation into clinics.

Dielectrophoresis-Based Biosensor for Detection of the Cancer Biomarkers CEA and CA 242 in Serum

We show that dielectrophoresis (DEP) spectroscopy is an effective transduction mechanism for detection of the concentration levels of the pancreatic cancer biomarkers cancer antigen (CA) 242 and carcinoembryonic antigen (CEA) in serum. We noticed a frequency dependence of the negative DEP force applied by interdigitated electrodes on functionalized polystyrene microspheres (PM) with respect to changes in the number of these cancer antigens bound to the PM. An electrode array with a well-defined gradient of the electric field was designed and used, which enabled the automation of the signal processing and reproducibility of the signal acquired by the biosensor.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Surface plasmon resonance: A promising approach for label-free early cancer diagnosis

Cancer is the second leading cause of death worldwide after cardiovascular disease. The major cause of high mortality is delayed detection. Therefore, detection at an early stage followed by early treatment can mitigate morbidity as well as mortality. The utilization of biomarker-based detection tools helps in early-stage recognition. Fortunately, biomarkers indicating disease status can be released in to the circulation. These include traditional marker proteins as well as exosomes, micro-RNA (miRNA) and circulating tumor DNA (ct-DNA). Biosensors are biological and chemical reaction devices that generate signals based on analyte concentration. Due to analyte binding, these devices demonstrate high sensitivity and specificity. This review examines the use of surface plasmon resonance (SPR)-based sensors in the diagnosis of various cancer including those of the breast, prostate, lung, ovary, cervix and pancreas. SPR is a label-free, real-time and non-invasive optical biosensing technology representing a novel diagnostic tool in cancer detection.

Review of Immunohistochemistry Biomarkers in Pancreatic Cancer Diagnosis

Pancreatic cancer is one of the cancer types with poor prognosis and high rate of mortality. Diagnostic modalities for early detection of pancreatic cancer have been among the academic concerns. On account of the potential role of immunohistochemistry (IHC) biomarkers in overcoming certain limitations of imaging diagnostic tools in discriminating pancreatic cancer tissues from benign ones, a growing scholarly attention has been given to the diagnostic efficacy of IHC biomarkers for pancreatic cancer. This review will analyze and synthesize published articles to provide an insight into potential IHC biomarkers for pancreatic cancer diagnosis.

Early Diagnostic Ability of Human Complement Factor B in Pancreatic Cancer Is Partly Linked to Its Potential Tumor-Promoting Role

Although plasma complement factor B (CFB, NX_P00751), both alone and in combination with CA19-9 (i.e., the ComB-CAN), previously exhibited a reliable diagnostic ability for pancreatic cancer (PC), its detectability of the early stages and the cancer detection mechanism remained elusive. We first evaluated the diagnostic accuracy of ComB-CAN using plasma samples from healthy donors (HDs), patients with chronic pancreatitis (CP), and patients with different PC stages (I/II vs III/IV). An analysis of the area under the curve (AUC) by PanelComposer using logistic regression revealed that ComB-CAN has a superior diagnostic ability for early-stage PC (97.1.% [95% confidence interval (CI): (97.1-97.2)]) compared with CFB (94.3% [95% CI: 94.2-94.4]) or CA19-9 alone (34.3% [95% CI: 34.1-34.4]). In the comparisons of all stages of patients with PC vs CP and HDs, the AUC values of ComB-CAN, CFB, and CA19-9 were 0.983 (95% CI: 0.983-0.983), 0.950 (95% CI: 0.950-0.951), and 0.873 (95% CI: 0.873-0.874), respectively. We then investigated the molecular mechanism underlying the detection of early-stage PC by using stable cell lines of CFB knockdown and CFB overexpression. A global transcriptomic analysis coupled to cell invasion assays of both CFB-modulated cell lines suggested that CFB plays a tumor-promoting role in PC, which likely initiates the PI3K-AKT cancer signaling pathway. Thus our study establishes ComB-CAN as a reliable early diagnostic marker for PC that can be clinically applied for early PC screening in the general public.

A personalized approach to pancreatic ductal adenocarcinoma and its application in surgical practice

Pancreatic duct adenocarcinoma is an aggressive tumor which constitutes the fourth leading cause of cancer-related mortality in the USA. Despite the fact that surgery is an integral part of treatment, 5-year survival rates remain unfavorable, partly because of the complex genetic background, delayed diagnosis and also the absence of effective therapeutic approaches. To optimize surgery's results in recent years, the use of patients' genetic profile has been implemented through classification into subtypes; subtypes based on mutations which could efficiently lead oncologists to the path of targeted novel neoadjuvant regimens. This approach aims to achieve the most effective selection of patients undergoing surgery, to increase the number of potentially resectable tumors and also control micro-metastases, aiming to extend overall survival.

Tips and tricks for the diagnosis and management of biliary stenosis-state of the art review

Biliary stenosis may represent a diagnostic and therapeutic challenge resulting in a delay in diagnosis and initiation of therapy due to the frequent difficulty in distinguishing a benign from a malignant stricture. In such cases, the diagnostic flowchart includes the sequential execution of imaging techniques, such as magnetic resonance, magnetic resonance cholangiopancreatography, and endoscopic ultrasound, while endoscopic retrograde cholangiopancreatography is performed to collect tissue for histopathological/cytological diagnosis or to treat the stenosis by insertion of stent. The execution of percutaneous transhepatic drainage with subsequent biopsy has been shown to increase the possibility of tissue diagnosis after failure of the above techniques. Although the diagnostic yield of histopathology and imaging has increased with improvements in endoscopic ultrasound and peroral cholangioscopy, differential diagnosis between malignant and benign stenosis may not be easy in some patients, and strictures are classified as indeterminate. In these cases, a multidisciplinary workup including biochemical marker assays and advanced technologies available may speed up a diagnosis of malignancy or avoid unnecessary surgery in the event of a benign stricture. Here, we review recent advancements in the diagnosis and management of biliary strictures and describe tips and tricks to increase diagnostic yields in clinical routine.

Development of a MicroRNA Signature Predictive of Recurrence and Survival in Pancreatic Ductal Adenocarcinoma

Simple Summary

Optimal patient selection for radiotherapy in pancreatic cancer is unestablished and may be improved with molecular profiling. To this end, we developed and validated a microRNA signature that predicted for worse locoregional recurrence and overall survival in patients with resectable pancreatic cancer. In a separate cohort of patients with borderline resectable and locally advanced pancreatic cancer, this risk signature was also predictive of worse locoregional recurrence, distant recurrence, and overall survival. Additionally, borderline resectable or locally advanced patients who had high risk score and did not receive radiation had worse outcomes compared to patients who either had low risk score or received radiation, irrespective of risk score. This risk signature may be useful in assessing patient prognosis and tailor therapy in patients with resectable, borderline resectable, or locally advanced pancreatic cancer, but requires further study.

Abstract

Background: Optimal patient selection for radiotherapy in pancreatic ductal adenocarcinoma (PDAC) is unestablished. Molecular profiling may select patients at high risk for locoregional recurrence (LRR) who would benefit from radiation. Methods: We included resectable pancreatic cancer (R-PDAC) patients, divided into training and validation cohorts, treated among three institutions with surgery and adjuvant chemotherapy, and borderline resectable or locally advanced pancreatic cancer (BR/LA-PDAC) patients treated with chemotherapy with or without radiation at the primary study institution. We isolated RNA from R-PDAC surgical specimens. Using NanoString, we identified miRNAs differentially expressed between normal and malignant pancreatic tissue. ElasticNet regression identified two miRNAs most predictive of LRR in the training cohort, miR-181b/d and miR-575, which were used to generate a risk score (RS). We evaluated the association of the median-dichotomized RS with recurrence and overall survival (OS). Results: We identified 183 R-PDAC and 77 BR/LA-PDAC patients with median follow up of 37 months treated between 2001 and 2014. On multivariable analysis of the R-PDAC training cohort (n = 90), RS was associated with worse LRR (HR = 1.34; 95%CI 1.27–11.38; p = 0.017) and OS (HR = 2.89; 95%CI 1.10–4.76; p = 0.027). In the R-PDAC validation cohort, RS was associated with worse LRR (HR = 2.39; 95%CI 1.03–5.54; p = 0.042), but not OS (p = 0.087). For BR/LA-PDAC, RS was associated with worse LRR (HR = 2.71; 95%CI 1.14–6.48; p = 0.025), DR (HR = 1.93; 95%CI 1.10–3.38; p = 0.022), and OS (HR = 1.97; 95%CI 1.17–3.34; p = 0.011). Additionally, after stratifying by RS and receipt of radiation in BR/LA-PDAC patients, high RS patients who did not receive radiation had worse LRR (p = 0.018), DR (p = 0.006), and OS (p < 0.001) compared to patients with either low RS or patients who received radiation, irrespective of RS. Conclusions: RS predicted worse LRR and OS in R-PDAC and worse LRR, DR, and OS in BR/LA-PDAC. This may select patients who would benefit from radiation and should be validated prospectively.

4H12, a Murine Monoclonal Antibody Directed against Myosin Heavy Chain-9 Expressed on Acinar Cell Carcinoma of Pancreas with Potential Therapeutic Application

Background:

PACC is a rare type of pancreatic exocrine neoplasm that is frequently diagnosed at late stages with a high rate of metastasis. Identification of new biomarkers for PACC can improve our knowledge of its biology, early detection, or targeted therapy. In this study, hybridoma technology was used to generate mAbs against Faraz-ICR, a pancreatic acinar cell carcinoma cell line.

Methods:

Cell ELISA and flow cytometry were used for screening, and the 4H12 hybridoma clone was selected for further analysis. The 4H12 mAb was specific for MYH9 as determined by Immunoprecipitation, Western blot, and mass spectrometry.

Results:

This antibody reacted variably with other cancer cells, in comparison to Faraz-ICR cell. Besides, by immunohistochemical staining, the acinar cell tumor, which was the source of Faraz-ICR, showed high MYH9 expression. Among 21 PDAC cases, nine (42.8%) expressed MYH9 with low intensity, while 10 (47.8%) and 2 (9.5%) cases expressed MYH9 with moderate to strong intensities, respectively. The 4H12 mAb inhibited the proliferation of Faraz-ICR cells in a dose-dependent manner from 0.75 to 12.5 μg/ml concentrations (p < 0.0001 and p < 0.002). IC₅₀ values were achieved at 12.09 ± 4.19 µg/ml and 7.74 ± 4.28 µg/ml after 24- and 48-h treatment, respectively.

Conclusion:

Our data suggest that the 4H12 mAb can serve as a tool for investigating the role of MYH9 pancreatic cancer biology and prognosis.

Adjuvant Treatment in Pancreatic Cancer: Shaping the Future of the Curative Setting

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease even in the early stages, despite progresses in surgical and pharmacological treatment in recent years. High potential for metastases is the main cause of therapeutic failure in localized disease, highlighting the current limited knowledge of underlying pathological processes. However, nowadays research is focusing on the search for personalized approaches also in the adjuvant setting for PDAC, by implementing the use of biomarkers and investigating new therapeutic targets. In this context, the aim of this narrative review is to summarize the current treatment scenario and new potential therapeutic approaches in early stage PDAC, from both a preclinical and clinical point of view. Additionally, the review examines the role of target therapies in localized PDAC and the influence of neoadjuvant treatments on survival outcomes.

Establishment of Pancreatobiliary Cancer Zebrafish Avatars for Chemotherapy Screening

Background: Cancers of the pancreas and biliary tree remain one of the most aggressive oncological malignancies, with most patients relying on systemic chemotherapy. However, effective biomarkers to predict the best therapy option for each patient are still lacking. In this context, an assay able to evaluate individual responses prior to treatment would be of great value for clinical decisions. Here we aimed to develop such a model using zebrafish xenografts to directly challenge pancreatic cancer cells to the available chemotherapies. Methods: Zebrafish xenografts were generated from a Panc-1 cell line to optimize the pancreatic setting. Pancreatic surgical resected samples, without in vitro expansion, were used to establish zebrafish patient-derived xenografts (zAvatars). Upon chemotherapy exposure, zAvatars were analyzed by single-cell confocal microscopy. Results: We show that Panc-1 zebrafish xenografts are able to reveal tumor responses to both FOLFIRINOX and gemcitabine plus nanoparticle albumin-bound (nab)-paclitaxel in just 4 days. Moreover, we established pancreatic and ampullary zAvatars with patient-derived tumors representative of different histological types. Conclusion: Altogether, we provide a short report showing the feasibility of generating and analyzing with single-cell resolution zAvatars from pancreatic and ampullary cancers, with potential use for future preclinical studies and personalized treatment.

Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review

Pancreatic cancer is a highly malignant digestive system tumor with a poor prognosis. Most pancreatic cancer patients are diagnosed at an advanced stage or even metastasis due to its highly aggressive characteristics and lack of typical early symptoms. Thus, an early diagnosis of pancreatic cancer is crucial for improving its prognosis. Currently, screening is often applied in high‐risk individuals to achieve the early diagnosis of pancreatic cancer. Fully understanding the risk factors of pancreatic cancer and pathogenesis could help us identify the high‐risk population and achieve early diagnosis and timely treatment of pancreatic cancer. Notably, accumulating studies have been undertaken to improve the detection rate of different imaging methods and the diagnostic accuracy of endoscopic ultrasound‐guided fine‐needle aspiration (EUS‐FNA) which is the golden standard for pancreatic cancer diagnosis. In addition, there are currently no biomarkers with sufficient sensitivity and specificity for the diagnosis of pancreatic cancer to be applied in the clinic. As the only serum biomarker approved by the United States Food and Drug Administration, carbohydrate antigen 19‐9 (CA19‐9) is not recommended to be used in the early screening of pancreatic cancer because of its limited specificity. Recently, increasing numbers of studies focused on the discovering of novel serum biomarkers and exploring their combination with CA19‐9 in the detection of pancreatic cancer. Besides, the application of liquid biopsy involving circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNAs (miRNAs), and exosomes in blood and biomarkers in urine, and saliva in pancreatic cancer diagnosis are drawing more and more attention. Furthermore, many innovative technologies such as artificial intelligence, computer‐aided diagnosis system, metabolomics technology, ion mobility spectrometry (IMS) associated technologies, and novel nanomaterials have been tested for the early diagnosis of pancreatic cancer and have shown promising prospects. Hence, this review aims to summarize the recent progress in the development of early screening and diagnostic methods, including imaging, pathological examination, serological examination, liquid biopsy, as well as other potential diagnostic strategies for pancreatic cancer.

Treatment Options for Germline BRCA-Mutated Metastatic Pancreatic Adenocarcinoma

Pancreatic cancer is the fourth leading cause of death from cancer in both men and women. Pancreatic cancer is typically diagnosed at an advanced stage and has an overall 5-year survival of approximately 9.3%. The National Comprehensive Cancer Network recommends both germline testing (testing cells such as blood or skin that do not have cancer) as well as somatic testing (testing cells with cancer) for pathogenic variants that may increase the risk of pancreatic cancer. In December 2019, the U.S. Food & Drug Administration approved the poly(ADP-ribose) polymerase (PARP) inhibitor olaparib for maintenance treatment of germline BRCA-mutated metastatic pancreatic adenocarcinoma in individuals who have completed at least 16 weeks of progression-free treatment with first-line platinum-based chemotherapy. This new therapy option has implications not only for treatment but also for the role of the oncology advanced practitioner as genetic testing becomes more prevalent in the care of patients with cancer.

Evaluation and Management of Malignant Biliary Obstruction

There is no reason to be pollyannaish when approaching patients with malignant biliary obstruction (MBO). Although technology has allowed refining diagnosis and resectability of cancers causing biliary obstruction, outcomes have not improved significantly. The previous preponderant place of surgical procedures now is replaced by endoluminal and percutaneous techniques for the management of symptoms of MBO. Because quantity of life often is the primary and sole outcome for evaluation of various interventions, the main focus of patient quality of life may be erroneously deemphasized. Lagging behind scientific advances are the availability of palliative care services and studies of patient-related outcomes.

Screening for pancreatic cancer: a review for general clinicians

Pancreatic cancer (PC) is an exceptionally lethal malignancy with increasing incidence and mortality worldwide. One of the principal challenges in the treatment of PC is that the diagnosis is usually made at a late stage when potentially curative surgical resection is no longer an option. General clinicians including internists and family physicians are well positioned to identify high-risk individuals and refer them to centers with expertise in PC screening and treatment where screening modalities can be employed. Here, we provide an up-to-date review of PC precursor lesions, epidemiology, and risk factors to empower the general clinician to recognize high-risk patients and employ risk reduction strategies. We also review current screening guidelines and modalities and preview progress that is being made to improve screening tests and biomarkers. It is our hope that this review article will empower the general clinician to understand which patients need to be screened for PC, strategies that may be used to reduce PC risk, and which screening modalities are available in order to diminish the lethality of PC.

Understanding the Clinical Impact of MUC5AC Expression on Pancreatic Ductal Adenocarcinoma

Simple Summary

Management of pancreatic cancer is challenging as there are limited treatment options, and most cases are diagnosed at advanced stages. In addition, there are no dependable tests available to predict bad outcomes or treatment responses in current clinical practice. Here, we shed light on the available evidence on mucin, MUC5AC in predicting the outcome of pancreatic cancers. We also discuss variants of MUC5AC believed to have a role in the malignant transformation of pancreatic tissues.

Abstract

Mucin-5AC (MUC5AC) is a heavily glycosylated gel-forming secreted mucin with a reliable prognostic value when detected in multiple malignancies. It is highly prevalent (70%) in PDA and is nonexistent in normal pancreatic tissues. Retrospective studies on PDA tumor tissue (detected by immunohistochemistry or IHC)) have investigated the prognostic value of MUC5AC expression but were equivocal. Some studies associated it with poor outcomes (survival or pathological features such as lymph node disease, vascular/neural invasion in resected tumors), while others have concluded that it is a good prognostic marker. The examination of expression level threshold (5%, 10%, or 25%) and the detected region (apical vs. cytoplasmic) were variable among the studies. The maturation stage and glycoform of MUC5AC detected also differed with the Monoclonal antibody (Mab) employed for IHC. CLH2 detects less mature/less glycosylated versions while 45M1 or 21-1 detect mature/more glycosylated forms. Interestingly, aberrantly glycosylated variants of MUC5AC were detected using lectin assays (Wheat Germ Agglutinin-MUC5AC), and Mabs such as NPC-1C and PAM4 have are more specific to malignant pancreatic tissues. NPC-1C and PAM4 antibody reactive epitopes on MUC5AC are immunogenic and could represent specific changes on the native MUC5AC glycoprotein linked to carcinogenesis. It was never studied to predict treatment response.

Deep Learning Improves Pancreatic Cancer Diagnosis Using RNA-Based Variants

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

Stroma-targeting strategies in pancreatic cancer: Past lessons, challenges and prospects

Pancreatic ductal adenocarcinoma (PDAC) is projected to emerge as the second leading cause of cancer-related death after 2030. Extreme treatment resistance is perhaps the most significant factor that underlies the poor prognosis of PDAC. To date, combination chemotherapy remains the mainstay of treatment for most PDAC patients. Compared to other cancer types, treatment response of PDAC tumors to similar chemotherapy regimens is clearly much lower and shorter-lived. Aside from typically harboring genetic alterations that to date remain un-druggable and are drivers of treatment resistance, PDAC tumors are uniquely characterized by a densely fibrotic stroma that has well-established roles in promoting cancer progression and treatment resistance. However, emerging evidence also suggests that indiscriminate targeting and near complete depletion of stroma may promote PDAC aggressiveness and lead to detrimental outcomes. These conflicting results undoubtedly warrant the need for a more in-depth understanding of the heterogeneity of tumor stroma in order to develop modulatory strategies in favor of tumor suppression. The advent of novel techniques including single cell RNA sequencing and multiplex immunohistochemistry have further illuminated the complex heterogeneity of tumor cells, stromal fibroblasts, and immune cells. This new knowledge is instrumental for development of more refined therapeutic strategies that can ultimately defeat this disease. Here, we provide a concise review on lessons learned from past stroma-targeting strategies, new challenges revealed from recent preclinical and clinical studies, as well as new prospects in the treatment of PDAC.

Pancreatic Cancer Insights: Optimization of the Diagnostic Capacity of Tumor Biomarkers

OBJECTIVE

Pancreatic cancer (PC) is one of the deadliest malignancies. The aim of this study was to determine the usefulness of the carbohydrate antigen 19.9 (CA19.9)/ carcinoembryonic antigen (CEA) ratio as a diagnostic tool.

METHODS

This was a retrospective observational study (2015-2019), including laboratory requests with increased CA19.9 and CEA but no previous neoplasia. Receiver operating characteristic (ROC) curve analyses were performed for the CA19.9/CEA ratio and for CA19.9 and CEA alone for the detection of PC, and cutoff values for all strategies were selected separately and in combination.

RESULTS

A total of 373 individuals were included. The area under the curve (AUC) for CA19.9/CEA was 0.872, whereas the AUC for CA19.9 was 0.847 and for CEA was 0.554. Cutoff values with the greatest diagnostic power were CA19.9/CEA >40, CA19.9 >1130 U/mL, and CEA > 14.5 U/mL. The combination of CA19.9/CEA > 40 with CA19.9 > 550 U/mL maximized the diagnostic accuracy for PC.

CONCLUSION

Our results highlight the relevance of the measurement of serum CA19.9 and CEA in the detection of PC.

Circulating tumor DNA (ctDNA) as a pan-cancer screening test: is it finally on the horizon?

The detection of cancer at an early stage while it is curable by surgical resection is widely believed to be one of the most effective strategies for reducing cancer mortality. Hence, the intense interests in the development of a simple pan-cancer screening test. Lack of sensitivity and specificity when combined with the low prevalence of most types of cancer types in the general population limit the use of most of the existing protein biomarkers for this purpose. Like proteins, tumor DNA also can be released into the circulation. Such circulating tumor DNA (ctDNA) can be differentiated from normal cell DNA by the presence of specific genetic alteration such as mutations, copy number changes, altered methylation patterns or being present in different sized fragments. Emerging results with test such as CancerSEEK or GRAIL suggest that the use of ctDNA can detect cancer with specificities >99%. Sensitivity however, is cancer type and stage-dependent, varying from approximately 40% in stage I disease to approximately 80% in stage III disease. It is important to stress however, that most of the studies published to date have used patients with an established diagnosis of cancer while the control population were healthy individuals. Although the emerging results are promising, evidence of clinical utility will require demonstration of reduced mortality following evaluation in a prospective randomized screening trial.

Review of clinical and emerging biomarkers for early diagnosis and treatment management of pancreatic cancer: towards personalised medicine

Background:

Pancreatic cancer is the 12th most commonly diagnosed cancer and the 3rd leading cause of cancer mortality and accounts for approximately 2·7% of all newly diagnosed cancer cases and 6·4% of all cancer mortalities in Canada. It has a very poor survival rate mainly due to the difficulty of detecting the disease at an early stage. Consequently, in the advancement of disease management towards the concept of precision medicine that takes individual patient variabilities into account, several investigators have focused on the identification of effective clinical biomarkers with high specificity and sensitivity, capable of early diagnosis of symptomatic patients and early detection of the disease in asymptomatic individuals at high risk for developing pancreatic cancer.

Materials and methods:

We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on biomarkers for the management of pancreatic cancer. In this narrative review paper, we describe 13 clinical and emerging biomarkers for pancreatic cancers used in screening for early detection and diagnosis, to identify patients’ risk for metastatic disease and subsequent relapse, to monitor patient response to specific treatment and to provide clinicians the possibility of prospectively identifying groups of patients who will benefit from a particular treatment.

Conclusions:

Current and emerging biomarkers for pancreatic cancer with high specificity and sensitivity has the potential to account for individual patient variabilities, for early detection of disease before the onset of metastasis to improve treatment outcome and patients’ survival, help screen high-risk populations, predict prognosis, provide accurate information of patient response to specific treatment and improve patients monitoring during treatment. Thus, the future holds promise for the use of effective clinical biomarkers or a panel of biomarkers for personalised patient-specific targeted medicine for pancreatic cancer.

Sweet systems: technologies for glycomic analysis and their integration into systems biology

Found in virtually every organism, glycans are essential molecules that play important roles in almost every aspect of biology. The composition of glycome, the repertoire of glycans in an organism or a biological sample, is often found altered in many diseases, including cancer, infectious diseases, metabolic and developmental disorders. Understanding how glycosylation and glycomic changes enriches our knowledge of the mechanisms of disease progression and sheds light on the development of novel therapeutics. However, the inherent diversity of glycan structures imposes challenges on the experimental characterization of glycomes. Advances in high-throughput glycomic technologies enable glycomic analysis in a rapid and comprehensive manner. In this review, we discuss the analytical methods currently used in high-throughput glycomics, including mass spectrometry, liquid chromatography and lectin microarray. Concomitant with the technical advances is the integration of glycomics into systems biology in the recent years. Herein we elaborate on some representative works from this recent trend to underline the important role of glycomics in such integrated approaches to disease.

An Integrated Data Analysis of mRNA, miRNA and Signaling Pathways in Pancreatic Cancer

Although many genes and miRNAs have been reported for various cancers, pancreatic cancer's specific genes or miRNAs have not been studied precisely yet. Therefore, we have analyzed the gene and miRNA expression profile of pancreatic cancer data in the gene expression omnibus (GEO) database. The microarray-derived miRNAs and mRNAs were annotated by gene ontology (GO) and signaling pathway analysis. We also recognized mRNAs that were targeted by miRNA through the mirDIP database. An integrated analysis of the microarray revealed that only 6 out of 43 common miRNAs had significant differences in their expression profiles between the tumor and normal groups (P value < 0.05 and |log Fold Changes (logFC)|> 1). The hsa-miR-210 had upregulation, whereas hsa-miR-375, hsa-miR-216a, hsa-miR-217, hsa-miR-216b and hsa-miR-634 had downregulation in pancreatic cancer (PC). The analysis results also revealed 109 common mRNAs by microarray and mirDIP 4.1 databases. Pathway analysis showed that amoebiasis, axon guidance, PI3K-Akt signaling pathway, absorption and focal adhesion, adherens junction, platelet activation, protein digestion, human papillomavirus infection, extracellular matrix (ECM) receptor interaction, and riboflavin metabolism played important roles in pancreatic cancer. GO analysis revealed the significant enrichment in the three terms of biological process, cellular component, and molecular function, which were identified as the most important processes associated strongly with pancreatic cancer. In conclusion, DTL, CDH11, COL5A1, ITGA2, KIF14, SMC4, VCAN, hsa-mir-210, hsa-mir-217, hsa-mir-216a, hsa-mir-216b, hsa-mir-375 and hsa-mir-634 can be reported as the novel diagnostic or even therapeutic markers for the future studies. Also, the hsa-mir-107 and hsa-mir-125a-5p with COL5A1, CDH11 and TGFBR1 genes can be introduced as major miRNA and genes on the miRNA-drug-mRNA network. The new regulatory network created in our study could give a deeper knowledge of the pancreatic cancer.

Circulating stromal cells in resectable pancreatic cancer correlates to pathological stage and predicts for poor clinical outcomes

Pancreatic cancer (PC) is notoriously difficult to diagnosis and properly stage resulting in incorrect primary treatment. Diagnostic and prognostic biomarkers are desperately needed to more accurately stage patients and select proper treatments. Recently, a newly discovered circulating stromal cell, i.e. cancer associated macrophage-like cell (CAML), was found to accurately identify solid cancers and predict for worse prognosis. In this pilot study, blood samples were procured from 63 PC patients prior to start of therapeutic intent. CAMLs were found in 95% of samples tested, with ≥12 CAMLs/7.5 mL and ≥50 µm CAMLs both predicting for advanced pathological stage and progression free survival. These data suggest that CAML assessment prior to treatment of PC predicts patients with under-staged disease and with more aggressive PC less likely to respond to standard of care treatment.

High expression of krüppel-like factor 10 or Smad4 predicts clinical benefit of adjuvant chemoradiotherapy in curatively resected pancreatic adenocarcinoma: From a randomized phase III trial

PURPOSE

Our previous studies have demonstrated that Krüppel-like factor 10 (Klf10) modulated tumor radiation resistance and helps to predict clinical outcomes of pancreatic adenocarcinoma (PDAC). This study aimed to evaluate whether the expression levels of Klf10, Smad4 and Runx3 can help predict the benefits of adjuvant chemoradiotherapy (CRT) in resected PDAC.

METHODS AND MATERIALS

Tissue specimens were collected from 111 patients with curatively resected PDAC who were enrolled into a randomized trial comparing adjuvant gemcitabine with or without CRT. Immunohistochemical expression of biomarkers was quantified by pathologists blinded to patient outcomes through a grading system based on the extent and intensity of staining. The predictive value of biomarkers was analyzed using SAS statistical software.

RESULTS

In total, 56 and 55 patients received adjuvant gemcitabine alone and additional CRT, respectively. The expression levels of Klf10, Smad4 and postoperative CA19-9 were significantly correlated with overall survival (OS) (p = 0.013, 0.045, and 0.047, respectively). Multivariable analysis showed that the expression level of postoperative serum CA19-9 and tumor tissue Klf10 expression level were significant predictors for OS (p = 0.038, and 0.028, respectively). Patients with high Klf10 or Smad4 (n = 55), had a significantly better local recurrence-free survival (∞ vs 19.8 months; p = 0.026) and a longer OS (33.0 vs 23.0 months; p = 0.12) if they received additional adjuvant CRT than gemcitabine only. The results were similar after adjusted by postoperative level of CA19-9.

CONCLUSION

Patients with curatively resected PDAC and a high expression of either Klf10 or Smad4 have high chances of benefiting from adjuvant CRT. Combining Klf10 and Smad4 to predict the benefits of adjuvant CRT in resected PDAC deserves further validation.

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.